Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations21435
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory176.0 B

Variable types

Numeric16
DateTime1
Categorical4

Alerts

bathrooms is highly overall correlated with grade and 3 other fieldsHigh correlation
bedrooms is highly overall correlated with sqft_above and 1 other fieldsHigh correlation
floors is highly overall correlated with yr_builtHigh correlation
grade is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
long is highly overall correlated with zipcodeHigh correlation
price is highly overall correlated with grade and 3 other fieldsHigh correlation
sqft_above is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_living15 is highly overall correlated with grade and 3 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
yr_built is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.2%)Imbalance
id has unique valuesUnique
sqft_basement has 13015 (60.7%) zerosZeros
yr_renovated has 20525 (95.8%) zerosZeros

Reproduction

Analysis started2025-10-31 05:44:19.857840
Analysis finished2025-10-31 05:44:43.500475
Duration23.64 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct21435
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.580867 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:43.568827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1000319 × 108
Q12.1237001 × 109
median3.9049212 × 109
Q37.3087501 × 109
95-th percentile9.2973006 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.18505 × 109

Descriptive statistics

Standard deviation2.8766182 × 109
Coefficient of variation (CV)0.62796372
Kurtosis-1.2606031
Mean4.580867 × 109
Median Absolute Deviation (MAD)2.4030788 × 109
Skewness0.24314969
Sum9.8190883 × 1013
Variance8.2749325 × 1018
MonotonicityStrictly increasing
2025-10-31T00:44:43.699696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99000001901
 
< 0.1%
10001021
 
< 0.1%
12000191
 
< 0.1%
12000211
 
< 0.1%
28000311
 
< 0.1%
36000571
 
< 0.1%
36000721
 
< 0.1%
38000081
 
< 0.1%
52000871
 
< 0.1%
62000171
 
< 0.1%
Other values (21425)21425
> 99.9%
ValueCountFrequency (%)
10001021
< 0.1%
12000191
< 0.1%
12000211
< 0.1%
28000311
< 0.1%
36000571
< 0.1%
36000721
< 0.1%
38000081
< 0.1%
52000871
< 0.1%
62000171
< 0.1%
72000801
< 0.1%
ValueCountFrequency (%)
99000001901
< 0.1%
98950000401
< 0.1%
98423005401
< 0.1%
98423004851
< 0.1%
98423000951
< 0.1%
98423000361
< 0.1%
98393011651
< 0.1%
98393010601
< 0.1%
98393010551
< 0.1%
98393008751
< 0.1%

date
Date

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size334.9 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-31T00:44:43.813318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:43.933194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

High correlation 

Distinct4000
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean541645.37
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:44.054568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile213500
Q1324844
median450000
Q3645000
95-th percentile1160000
Maximum7700000
Range7625000
Interquartile range (IQR)320156

Descriptive statistics

Standard deviation367322.88
Coefficient of variation (CV)0.67816121
Kurtosis34.72392
Mean541645.37
Median Absolute Deviation (MAD)150000
Skewness4.0360699
Sum1.1610169 × 1010
Variance1.349261 × 1011
MonotonicityNot monotonic
2025-10-31T00:44:44.184331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000172
 
0.8%
350000167
 
0.8%
550000157
 
0.7%
500000151
 
0.7%
425000149
 
0.7%
325000146
 
0.7%
400000144
 
0.7%
375000137
 
0.6%
300000131
 
0.6%
525000128
 
0.6%
Other values (3990)19953
93.1%
ValueCountFrequency (%)
750001
< 0.1%
780001
< 0.1%
800001
< 0.1%
810001
< 0.1%
825001
< 0.1%
830001
< 0.1%
840001
< 0.1%
850001
< 0.1%
890001
< 0.1%
899501
< 0.1%
ValueCountFrequency (%)
77000001
< 0.1%
70625001
< 0.1%
68850001
< 0.1%
55700001
< 0.1%
53500001
< 0.1%
53000001
< 0.1%
51108001
< 0.1%
46680001
< 0.1%
45000001
< 0.1%
44890001
< 0.1%

bedrooms
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3701889
Minimum0
Maximum11
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:44.280015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90692006
Coefficient of variation (CV)0.26910066
Kurtosis1.8525996
Mean3.3701889
Median Absolute Deviation (MAD)1
Skewness0.51796722
Sum72240
Variance0.822504
MonotonicityNot monotonic
2025-10-31T00:44:44.358872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
39731
45.4%
46849
32.0%
22736
 
12.8%
51586
 
7.4%
6265
 
1.2%
1194
 
0.9%
738
 
0.2%
813
 
0.1%
013
 
0.1%
96
 
< 0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
013
 
0.1%
1194
 
0.9%
22736
 
12.8%
39731
45.4%
46849
32.0%
51586
 
7.4%
6265
 
1.2%
738
 
0.2%
813
 
0.1%
96
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
103
 
< 0.1%
96
 
< 0.1%
813
 
0.1%
738
 
0.2%
6265
 
1.2%
51586
 
7.4%
46849
32.0%
39731
45.4%
22736
 
12.8%

bathrooms
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7517145
Minimum0
Maximum8
Zeros85
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:44.435577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.73485532
Coefficient of variation (CV)0.41950633
Kurtosis1.998447
Mean1.7517145
Median Absolute Deviation (MAD)1
Skewness0.90060207
Sum37548
Variance0.54001234
MonotonicityNot monotonic
2025-10-31T00:44:44.517465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
210481
48.9%
18253
38.5%
32217
 
10.3%
4335
 
1.6%
085
 
0.4%
548
 
0.2%
612
 
0.1%
72
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
085
 
0.4%
18253
38.5%
210481
48.9%
32217
 
10.3%
4335
 
1.6%
548
 
0.2%
612
 
0.1%
72
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
72
 
< 0.1%
612
 
0.1%
548
 
0.2%
4335
 
1.6%
32217
 
10.3%
210481
48.9%
18253
38.5%
085
 
0.4%

sqft_living
Real number (ℝ)

High correlation 

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2082.7265
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:44.623930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11430
median1920
Q32550
95-th percentile3770
Maximum13540
Range13250
Interquartile range (IQR)1120

Descriptive statistics

Standard deviation919.16248
Coefficient of variation (CV)0.44132653
Kurtosis5.2487448
Mean2082.7265
Median Absolute Deviation (MAD)550
Skewness1.4709482
Sum44643243
Variance844859.66
MonotonicityNot monotonic
2025-10-31T00:44:44.735315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300136
 
0.6%
1440133
 
0.6%
1400132
 
0.6%
1800128
 
0.6%
1660128
 
0.6%
1820127
 
0.6%
1560124
 
0.6%
1010124
 
0.6%
1540122
 
0.6%
1720122
 
0.6%
Other values (1028)20159
94.0%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3841
< 0.1%
3902
< 0.1%
4101
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
ValueCountFrequency (%)
135401
< 0.1%
120501
< 0.1%
100401
< 0.1%
98901
< 0.1%
96401
< 0.1%
92001
< 0.1%
86701
< 0.1%
80201
< 0.1%
80101
< 0.1%
80001
< 0.1%

sqft_lot
Real number (ℝ)

High correlation 

Distinct9782
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15136.064
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:44.841316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7614
Q310696.5
95-th percentile43560
Maximum1651359
Range1650839
Interquartile range (IQR)5656.5

Descriptive statistics

Standard deviation41539.543
Coefficient of variation (CV)2.7444085
Kurtosis284.07091
Mean15136.064
Median Absolute Deviation (MAD)2616
Skewness13.043382
Sum3.2444153 × 108
Variance1.7255336 × 109
MonotonicityNot monotonic
2025-10-31T00:44:44.945138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000355
 
1.7%
6000285
 
1.3%
4000249
 
1.2%
7200218
 
1.0%
4800118
 
0.6%
7500118
 
0.6%
4500112
 
0.5%
8400109
 
0.5%
9600108
 
0.5%
3600102
 
0.5%
Other values (9772)19661
91.7%
ValueCountFrequency (%)
5201
< 0.1%
5721
< 0.1%
6001
< 0.1%
6091
< 0.1%
6351
< 0.1%
6381
< 0.1%
6492
< 0.1%
6511
< 0.1%
6751
< 0.1%
6761
< 0.1%
ValueCountFrequency (%)
16513591
< 0.1%
11647941
< 0.1%
10742181
< 0.1%
10240681
< 0.1%
9829981
< 0.1%
9822781
< 0.1%
9204231
< 0.1%
8816541
< 0.1%
8712002
< 0.1%
8433091
< 0.1%

floors
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.9 KiB
1
12446 
2
8370 
3
 
619

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21435
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
112446
58.1%
28370
39.0%
3619
 
2.9%

Length

2025-10-31T00:44:45.039221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T00:44:45.119684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
112446
58.1%
28370
39.0%
3619
 
2.9%

Most occurring characters

ValueCountFrequency (%)
112446
58.1%
28370
39.0%
3619
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
112446
58.1%
28370
39.0%
3619
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
112446
58.1%
28370
39.0%
3619
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
112446
58.1%
28370
39.0%
3619
 
2.9%

waterfront
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.9 KiB
0
21272 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21435
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
021272
99.2%
1163
 
0.8%

Length

2025-10-31T00:44:45.200877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T00:44:45.273588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
021272
99.2%
1163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
021272
99.2%
1163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
021272
99.2%
1163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
021272
99.2%
1163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
021272
99.2%
1163
 
0.8%

view
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.9 KiB
0
19319 
2
 
962
3
 
507
1
 
331
4
 
316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21435
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019319
90.1%
2962
 
4.5%
3507
 
2.4%
1331
 
1.5%
4316
 
1.5%

Length

2025-10-31T00:44:45.349729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T00:44:45.429226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
019319
90.1%
2962
 
4.5%
3507
 
2.4%
1331
 
1.5%
4316
 
1.5%

Most occurring characters

ValueCountFrequency (%)
019319
90.1%
2962
 
4.5%
3507
 
2.4%
1331
 
1.5%
4316
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
019319
90.1%
2962
 
4.5%
3507
 
2.4%
1331
 
1.5%
4316
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
019319
90.1%
2962
 
4.5%
3507
 
2.4%
1331
 
1.5%
4316
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
019319
90.1%
2962
 
4.5%
3507
 
2.4%
1331
 
1.5%
4316
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.9 KiB
3
13911 
4
5645 
5
1686 
2
 
164
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21435
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
313911
64.9%
45645
26.3%
51686
 
7.9%
2164
 
0.8%
129
 
0.1%

Length

2025-10-31T00:44:45.514532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T00:44:45.597616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
313911
64.9%
45645
26.3%
51686
 
7.9%
2164
 
0.8%
129
 
0.1%

Most occurring characters

ValueCountFrequency (%)
313911
64.9%
45645
26.3%
51686
 
7.9%
2164
 
0.8%
129
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
313911
64.9%
45645
26.3%
51686
 
7.9%
2164
 
0.8%
129
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
313911
64.9%
45645
26.3%
51686
 
7.9%
2164
 
0.8%
129
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
313911
64.9%
45645
26.3%
51686
 
7.9%
2164
 
0.8%
129
 
0.1%

grade
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6617681
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:45.677128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1742752
Coefficient of variation (CV)0.15326426
Kurtosis1.1901124
Mean7.6617681
Median Absolute Deviation (MAD)1
Skewness0.77028604
Sum164230
Variance1.3789222
MonotonicityNot monotonic
2025-10-31T00:44:45.756341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
78895
41.5%
86044
28.2%
92606
 
12.2%
61995
 
9.3%
101130
 
5.3%
11396
 
1.8%
5234
 
1.1%
1289
 
0.4%
429
 
0.1%
1313
 
0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
33
 
< 0.1%
429
 
0.1%
5234
 
1.1%
61995
 
9.3%
78895
41.5%
86044
28.2%
92606
 
12.2%
101130
 
5.3%
11396
 
1.8%
ValueCountFrequency (%)
1313
 
0.1%
1289
 
0.4%
11396
 
1.8%
101130
 
5.3%
92606
 
12.2%
86044
28.2%
78895
41.5%
61995
 
9.3%
5234
 
1.1%
429
 
0.1%

sqft_above
Real number (ℝ)

High correlation 

Distinct946
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1790.9955
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:45.849158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11200
median1560
Q32220
95-th percentile3400
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation829.02996
Coefficient of variation (CV)0.4628878
Kurtosis3.3949976
Mean1790.9955
Median Absolute Deviation (MAD)450
Skewness1.4441781
Sum38389988
Variance687290.68
MonotonicityNot monotonic
2025-10-31T00:44:45.950469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300210
 
1.0%
1010204
 
1.0%
1200203
 
0.9%
1220186
 
0.9%
1140183
 
0.9%
1400180
 
0.8%
1180177
 
0.8%
1060177
 
0.8%
1340174
 
0.8%
1250173
 
0.8%
Other values (936)19568
91.3%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3841
< 0.1%
3902
< 0.1%
4101
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
ValueCountFrequency (%)
94101
< 0.1%
88601
< 0.1%
85701
< 0.1%
80201
< 0.1%
78801
< 0.1%
78501
< 0.1%
76801
< 0.1%
74201
< 0.1%
73201
< 0.1%
67201
< 0.1%

sqft_basement
Real number (ℝ)

Zeros 

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.73105
Minimum0
Maximum4820
Zeros13015
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:46.055742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.78793
Coefficient of variation (CV)1.517795
Kurtosis2.7121305
Mean291.73105
Median Absolute Deviation (MAD)0
Skewness1.5769613
Sum6253255
Variance196061.16
MonotonicityNot monotonic
2025-10-31T00:44:46.166020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013015
60.7%
600220
 
1.0%
700215
 
1.0%
500211
 
1.0%
800206
 
1.0%
400184
 
0.9%
1000147
 
0.7%
900143
 
0.7%
300141
 
0.7%
200106
 
0.5%
Other values (296)6847
31.9%
ValueCountFrequency (%)
013015
60.7%
101
 
< 0.1%
201
 
< 0.1%
404
 
< 0.1%
5011
 
0.1%
6010
 
< 0.1%
651
 
< 0.1%
707
 
< 0.1%
8020
 
0.1%
9021
 
0.1%
ValueCountFrequency (%)
48201
< 0.1%
41301
< 0.1%
35001
< 0.1%
34801
< 0.1%
32601
< 0.1%
30001
< 0.1%
28501
< 0.1%
28101
< 0.1%
27301
< 0.1%
27201
< 0.1%

yr_built
Real number (ℝ)

High correlation 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.0996
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:46.273084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11952
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.385501
Coefficient of variation (CV)0.014908177
Kurtosis-0.6542564
Mean1971.0996
Median Absolute Deviation (MAD)23
Skewness-0.4746777
Sum42250519
Variance863.50768
MonotonicityNot monotonic
2025-10-31T00:44:46.386557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014559
 
2.6%
2006454
 
2.1%
2005450
 
2.1%
2004429
 
2.0%
2003422
 
2.0%
2007415
 
1.9%
1977415
 
1.9%
1978384
 
1.8%
1968379
 
1.8%
2008367
 
1.7%
Other values (106)17161
80.1%
ValueCountFrequency (%)
190086
0.4%
190129
 
0.1%
190227
 
0.1%
190345
0.2%
190444
0.2%
190574
0.3%
190691
0.4%
190765
0.3%
190886
0.4%
190994
0.4%
ValueCountFrequency (%)
201538
 
0.2%
2014559
2.6%
2013199
 
0.9%
2012169
 
0.8%
2011130
 
0.6%
2010143
 
0.7%
2009229
1.1%
2008367
1.7%
2007415
1.9%
2006454
2.1%

yr_renovated
Real number (ℝ)

Zeros 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.733753
Minimum0
Maximum2015
Zeros20525
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:46.502447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation402.43998
Coefficient of variation (CV)4.7494649
Kurtosis18.60965
Mean84.733753
Median Absolute Deviation (MAD)0
Skewness4.5394263
Sum1816268
Variance161957.94
MonotonicityNot monotonic
2025-10-31T00:44:46.614361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020525
95.8%
201491
 
0.4%
201337
 
0.2%
200336
 
0.2%
200535
 
0.2%
200735
 
0.2%
200035
 
0.2%
200426
 
0.1%
200624
 
0.1%
199024
 
0.1%
Other values (60)567
 
2.6%
ValueCountFrequency (%)
020525
95.8%
19341
 
< 0.1%
19402
 
< 0.1%
19441
 
< 0.1%
19453
 
< 0.1%
19462
 
< 0.1%
19481
 
< 0.1%
19502
 
< 0.1%
19511
 
< 0.1%
19533
 
< 0.1%
ValueCountFrequency (%)
201516
 
0.1%
201491
0.4%
201337
0.2%
201211
 
0.1%
201113
 
0.1%
201018
 
0.1%
200922
 
0.1%
200818
 
0.1%
200735
 
0.2%
200624
 
0.1%

zipcode
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.861
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:46.727449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398117
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)84

Descriptive statistics

Standard deviation53.470342
Coefficient of variation (CV)0.00054518259
Kurtosis-0.84971172
Mean98077.861
Median Absolute Deviation (MAD)42
Skewness0.40818655
Sum2.102299 × 109
Variance2859.0775
MonotonicityNot monotonic
2025-10-31T00:44:46.844520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103599
 
2.8%
98038587
 
2.7%
98115576
 
2.7%
98052571
 
2.7%
98117548
 
2.6%
98042547
 
2.6%
98034543
 
2.5%
98118499
 
2.3%
98023492
 
2.3%
98006490
 
2.3%
Other values (60)15983
74.6%
ValueCountFrequency (%)
98001359
1.7%
98002197
0.9%
98003276
1.3%
98004315
1.5%
98005168
 
0.8%
98006490
2.3%
98007139
 
0.6%
98008283
1.3%
9801099
 
0.5%
98011194
 
0.9%
ValueCountFrequency (%)
98199316
1.5%
98198275
1.3%
98188135
 
0.6%
98178258
1.2%
98177254
1.2%
98168264
1.2%
98166250
1.2%
98155442
2.1%
9814856
 
0.3%
98146281
1.3%

lat
Real number (ℝ)

Distinct5034
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.56015
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:46.962441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.4711
median47.572
Q347.678
95-th percentile47.74963
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.2069

Descriptive statistics

Standard deviation0.1386018
Coefficient of variation (CV)0.0029142423
Kurtosis-0.67364154
Mean47.56015
Median Absolute Deviation (MAD)0.1047
Skewness-0.48808973
Sum1019451.8
Variance0.01921046
MonotonicityNot monotonic
2025-10-31T00:44:47.070380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.532217
 
0.1%
47.662417
 
0.1%
47.684617
 
0.1%
47.549117
 
0.1%
47.695516
 
0.1%
47.688616
 
0.1%
47.671116
 
0.1%
47.68615
 
0.1%
47.664715
 
0.1%
47.540215
 
0.1%
Other values (5024)21274
99.2%
ValueCountFrequency (%)
47.15591
< 0.1%
47.15931
< 0.1%
47.16221
< 0.1%
47.16471
< 0.1%
47.17641
< 0.1%
47.17751
< 0.1%
47.17762
< 0.1%
47.17951
< 0.1%
47.18031
< 0.1%
47.18081
< 0.1%
ValueCountFrequency (%)
47.77763
< 0.1%
47.77753
< 0.1%
47.77741
 
< 0.1%
47.77723
< 0.1%
47.77712
 
< 0.1%
47.7772
 
< 0.1%
47.77693
< 0.1%
47.77682
 
< 0.1%
47.77676
< 0.1%
47.77664
< 0.1%

long
Real number (ℝ)

High correlation 

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.21369
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21435
Negative (%)100.0%
Memory size334.9 KiB
2025-10-31T00:44:47.179812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.124
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.204

Descriptive statistics

Standard deviation0.14089699
Coefficient of variation (CV)-0.001152874
Kurtosis1.0454454
Mean-122.21369
Median Absolute Deviation (MAD)0.101
Skewness0.88185586
Sum-2619650.5
Variance0.019851962
MonotonicityNot monotonic
2025-10-31T00:44:47.288901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29114
 
0.5%
-122.3110
 
0.5%
-122.362102
 
0.5%
-122.291100
 
0.5%
-122.36399
 
0.5%
-122.37298
 
0.5%
-122.35795
 
0.4%
-122.28895
 
0.4%
-122.36594
 
0.4%
-122.34693
 
0.4%
Other values (742)20435
95.3%
ValueCountFrequency (%)
-122.5191
 
< 0.1%
-122.5151
 
< 0.1%
-122.5141
 
< 0.1%
-122.5121
 
< 0.1%
-122.5112
< 0.1%
-122.5092
< 0.1%
-122.5071
 
< 0.1%
-122.5061
 
< 0.1%
-122.5053
< 0.1%
-122.5042
< 0.1%
ValueCountFrequency (%)
-121.3152
< 0.1%
-121.3161
< 0.1%
-121.3191
< 0.1%
-121.3211
< 0.1%
-121.3251
< 0.1%
-121.3522
< 0.1%
-121.3591
< 0.1%
-121.3642
< 0.1%
-121.4021
< 0.1%
-121.4031
< 0.1%

sqft_living15
Real number (ℝ)

High correlation 

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1988.3451
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:47.395942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32370
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)880

Descriptive statistics

Standard deviation685.70034
Coefficient of variation (CV)0.34485983
Kurtosis1.5932641
Mean1988.3451
Median Absolute Deviation (MAD)410
Skewness1.1057971
Sum42620177
Variance470184.96
MonotonicityNot monotonic
2025-10-31T00:44:47.496751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540193
 
0.9%
1440190
 
0.9%
1560190
 
0.9%
1500179
 
0.8%
1460168
 
0.8%
1800166
 
0.8%
1580165
 
0.8%
1720165
 
0.8%
1610164
 
0.8%
1760163
 
0.8%
Other values (767)19692
91.9%
ValueCountFrequency (%)
3991
 
< 0.1%
4602
 
< 0.1%
6202
 
< 0.1%
6701
 
< 0.1%
6902
 
< 0.1%
7002
 
< 0.1%
7102
 
< 0.1%
7202
 
< 0.1%
7408
< 0.1%
7503
 
< 0.1%
ValueCountFrequency (%)
62101
 
< 0.1%
61101
 
< 0.1%
57906
< 0.1%
56101
 
< 0.1%
56001
 
< 0.1%
55001
 
< 0.1%
53801
 
< 0.1%
53401
 
< 0.1%
53301
 
< 0.1%
52201
 
< 0.1%

sqft_lot15
Real number (ℝ)

High correlation 

Distinct8689
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12786.339
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2025-10-31T00:44:47.602113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1989.4
Q15100
median7620
Q310087.5
95-th percentile37197.6
Maximum871200
Range870549
Interquartile range (IQR)4987.5

Descriptive statistics

Standard deviation27376.05
Coefficient of variation (CV)2.1410391
Kurtosis150.31762
Mean12786.339
Median Absolute Deviation (MAD)2508
Skewness9.4951763
Sum2.7407517 × 108
Variance7.4944813 × 108
MonotonicityNot monotonic
2025-10-31T00:44:47.705363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000425
 
2.0%
4000355
 
1.7%
6000286
 
1.3%
7200210
 
1.0%
4800144
 
0.7%
7500142
 
0.7%
8400115
 
0.5%
4500110
 
0.5%
3600110
 
0.5%
5100109
 
0.5%
Other values (8679)19429
90.6%
ValueCountFrequency (%)
6511
 
< 0.1%
6591
 
< 0.1%
6601
 
< 0.1%
7482
< 0.1%
7504
< 0.1%
7551
 
< 0.1%
7571
 
< 0.1%
7581
 
< 0.1%
7881
 
< 0.1%
7941
 
< 0.1%
ValueCountFrequency (%)
8712001
< 0.1%
8581321
< 0.1%
5606171
< 0.1%
4382131
< 0.1%
4347281
< 0.1%
4255811
< 0.1%
4229671
< 0.1%
4119621
< 0.1%
3920402
< 0.1%
3868121
< 0.1%

Interactions

2025-10-31T00:44:41.459398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:20.853194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:22.063773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:23.333179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:25.935758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:27.228143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:28.523096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:29.795465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:31.252748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:32.435866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:33.646507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:34.889440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:36.526895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:37.820372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:39.046744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:40.251607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:41.531261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:20.926408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:22.147996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:23.404735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-10-31T00:44:36.295222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:37.583801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:38.826237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:40.036490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:41.241478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:42.953644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:21.918637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:23.179790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:25.790797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:27.075540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:28.368362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:29.646798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:31.112188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:32.295298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:33.505058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:34.740512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:36.372386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:37.662122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:38.903400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:40.108908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:41.317070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:43.024342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:21.991387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:23.259357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:25.864639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:27.152962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:28.446588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:29.722410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:31.183650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:32.364908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:33.577037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:34.818365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:36.450561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:37.746352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:38.976566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:40.181221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-31T00:44:41.388447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-10-31T00:44:47.789888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
bathroomsbedroomsconditionfloorsgradeidlatlongpricesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotsqft_lot15viewwaterfrontyr_builtyr_renovatedzipcode
bathrooms1.0000.4690.1070.3900.5940.0080.0130.2190.4530.6270.1650.6640.4960.0470.0400.1080.1000.5060.052-0.170
bedrooms0.4691.0000.0510.1950.3820.006-0.0230.1920.3450.5400.2300.6480.4450.2180.2020.0500.0260.1790.017-0.168
condition0.1070.0511.0000.2480.1530.0300.0570.0810.0230.1070.0940.0600.0620.0410.0140.0240.0170.2490.0680.073
floors0.3900.1950.2481.0000.3730.0600.1800.2300.1460.4290.1780.3000.2940.0180.0150.0210.0170.5250.0240.246
grade0.5940.3820.1530.3731.0000.0180.1030.2210.6570.7120.0920.7160.6620.1530.1570.1420.1180.4990.016-0.179
id0.0080.0060.0300.0600.0181.000-0.0050.0060.0020.0020.0010.000-0.001-0.117-0.1150.0280.0060.026-0.018-0.005
lat0.013-0.0230.0570.1800.103-0.0051.000-0.1440.456-0.0270.1140.0290.026-0.121-0.1160.0680.034-0.1260.0250.249
long0.2190.1920.0810.2300.2210.006-0.1441.0000.0610.385-0.2010.2840.3800.3710.3730.0850.0970.411-0.076-0.578
price0.4530.3450.0230.1460.6570.0020.4560.0611.0000.5410.2520.6440.5720.0760.0640.2080.3200.0980.102-0.006
sqft_above0.6270.5400.1070.4290.7120.002-0.0270.3850.5411.000-0.1670.8440.6970.2730.2550.0890.0820.4710.030-0.277
sqft_basement0.1650.2300.0940.1780.0920.0010.114-0.2010.252-0.1671.0000.3270.1290.0370.0310.1590.135-0.1790.0630.115
sqft_living0.6640.6480.0600.3000.7160.0000.0290.2840.6440.8440.3271.0000.7470.3060.2850.1480.1400.3510.052-0.206
sqft_living150.4960.4450.0620.2940.662-0.0010.0260.3800.5720.6970.1290.7471.0000.3610.3670.1460.0890.334-0.006-0.287
sqft_lot0.0470.2180.0410.0180.153-0.117-0.1210.3710.0760.2730.0370.3060.3611.0000.9230.0400.014-0.0390.008-0.320
sqft_lot150.0400.2020.0140.0150.157-0.115-0.1160.3730.0640.2550.0310.2850.3670.9231.0000.0350.000-0.0180.009-0.327
view0.1080.0500.0240.0210.1420.0280.0680.0850.2080.0890.1590.1480.1460.0400.0351.0000.5950.0420.1090.075
waterfront0.1000.0260.0170.0170.1180.0060.0340.0970.3200.0820.1350.1400.0890.0140.0000.5951.0000.0320.0920.080
yr_built0.5060.1790.2490.5250.4990.026-0.1260.4110.0980.471-0.1790.3510.334-0.039-0.0180.0420.0321.000-0.215-0.315
yr_renovated0.0520.0170.0680.0240.016-0.0180.025-0.0760.1020.0300.0630.052-0.0060.0080.0090.1090.092-0.2151.0000.062
zipcode-0.170-0.1680.0730.246-0.179-0.0050.249-0.578-0.006-0.2770.115-0.206-0.287-0.320-0.3270.0750.080-0.3150.0621.000

Missing values

2025-10-31T00:44:43.134367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-31T00:44:43.374503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
2497100010220150422T00000030000063240093732003724000199109800247.3262-122.21420607316
6735120001920140508T00000064750041206026036100481160900194709816647.4444-122.351259021891
8411120002120140811T000000400000311460430001003714600195209816647.4434-122.347225020023
8809280003120150401T0000002350003114307599100461010420193009816847.4783-122.265129010320
3557360005720150319T000000402500421650350410037760890195120139814447.5803-122.29414803504
18521360007220150330T00000068000042222053101005711701050195109814447.5801-122.29415404200
3200380000820150224T000000178000511990182001003719900196009817847.4938-122.26218608658
21063520008720140709T00000048700042254050012003925400200509810847.5423-122.30223606834
4333620001720141112T000000281000311340213361004513400194509803247.4023-122.273134037703
16715720008020141104T000000239000421980105851002619800192409805547.4836-122.21413607810
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
1716983930087520140514T00000080000031170044001004817000190609812247.6120-122.29216104180
4679983930105520140626T00000067000031149044001004714900190609812247.6113-122.29215604400
4821983930106020150406T00000065050031174044001003817400190309812247.6115-122.29217404400
13028983930116520141001T00000099850021157044001004815700191409812247.6112-122.29318504400
11654984230003620141008T00000041588531131041631004713100196409812647.5301-122.38111204166
16737984230009520140725T00000036500052160041681003716000192709812647.5297-122.38111904168
3260984230048520150311T000000380000211040737210057840200193909812647.5285-122.37819305150
7621984230054020140624T000000339000311100412810047720380194209812647.5296-122.37915104538
20979989500004020140703T000000399900211410100510039900510201109802747.5446-122.01814401188
15951990000019020141030T000000268950311320810010036880440194309816647.4697-122.35110008100